Dataset Information

The short-term economic consequences of COVID-19: Exposure to disease, remote work and government response.

ABSTRACT: We examine the determinants of the consequences of COVID-19 on employment and wages in the United States. Guided by a pre-analysis plan, we investigate whether the economic consequences of COVID-19 were larger for certain occupations, using four indexes: workers relatively more exposed to disease, workers that work with proximity to coworkers, essential/critical workers and workers who can easily work remotely. We find that individuals that work in proximity to others are more affected while individuals able to work remotely and essential workers are less affected by the pandemic. We also present suggestive evidence that our indexes are likely explanations why certain demographic groups such as younger and minority workers have worse labor market outcomes during the pandemic.

SUBMITTER: Beland LP

PROVIDER: S-EPMC10016649 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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The short-term economic consequences of COVID-19: Exposure to disease, remote work and government response.

Beland Louis-Philippe LP Brodeur Abel A Wright Taylor T

PloS one 20230315 3

We examine the determinants of the consequences of COVID-19 on employment and wages in the United States. Guided by a pre-analysis plan, we investigate whether the economic consequences of COVID-19 were larger for certain occupations, using four indexes: workers relatively more exposed to disease, workers that work with proximity to coworkers, essential/critical workers and workers who can easily work remotely. We find that individuals that work in proximity to others are more affected while ind ...[more]

PMID: 36920940

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Project description:InstructionTo help determine the health protectiveness of government regulations and policies for air pollutant control for Americans, our study aimed to investigate the health and economic impacts of work loss due to sickness associated with daily all-source and wildfire-specific PM2.5 (particulate matter with an aerodynamic diameter smaller than 2.5 μm) exposures in California.MethodsWe linked the 2015-2018 California Health Interview Survey respondents' geocoded home addresses to daily PM2.5 estimated by satellites and atmospheric modelling simulations and wildfire-related PM2.5 from Community Multiscale Air Quality models. We calculated and applied the coefficient for the association between daily PM2.5 exposure and work loss from regression analyses to the Environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE) platform to assess the health and economic impacts of PM2.5 exposure on work loss due to sickness.ResultsWe observed that each 1 µg/m3 increase in daily total PM2.5 exposure will lead to about 1 million days of work loss per year ranging from 1.1 to 1.6 million person-days, and the related economic loss was $310-390 million. Wildfire smoke alone could contribute to 0.7-2.6 million work-loss days with a related economic loss of $129-521 million per year in 2015-2018. Using the function coefficient in the current BenMAP, the excess work-loss days due to sickness was about 250 000 days and the estimated economic loss was about $45-50 million for each 1 µg/m3 increase in daily total PM2.5 exposure, and wildfire smoke alone would lead to 0.17-0.67 million work-loss days with related economic loss of $31-128 million per year during the same period.ConclusionsBoth conventional and wildfire-specific sources of PM2.5 produced substantial work loss and cost in California. Updating the current BenMAP-CE calculations for work-loss days will be essential in quantifying the current health impacts of PM2.5 to help inform the policies and regulations to protect public health.

Project description:BackgroundCOVID-19 caused a worldwide outbreak leading the majority of human activities to a rough breakdown. Many stakeholders proposed multiple interventions to slow down the disease and number of papers were devoted to the understanding the pandemic, but to a less extend some were oriented socio-economic analysis. In this paper, a socio-economic analysis is proposed to investigate the early-age effect of socio-economic factors on COVID-19 spread.MethodsFifty-two countries were selected for this study. A cascade algorithm was developed to extract the R0 number and the day J*; these latter should decrease as the pandemic flattens. Subsequently, R0 and J* were modeled according to socio-economic factors using multilinear stepwise-regression.ResultsThe findings demonstrated that low values of days before lockdown should flatten the pandemic by reducing J*. Hopefully, DBLD is only parameter to be tuned in the short-term; the other socio-economic parameters cannot easily be handled as they are annually updated. Furthermore, it was highlighted that the elderly is also a major influencing factor especially because it is involved in the interactions terms in R0 model. Simulations proved that the health care system could improve the pandemic damping for low elderly. In contrast, above a given elderly, the reproduction number R0 cannot be reduced even for developed countries (showing high HCI values), meaning that the disease's severity cannot be smoothed regardless the performance of the corresponding health care system; non-pharmaceutical interventions are then expected to be more efficient than corrective measures.DiscussionThe relationship between the socio-economic factors and the pandemic parameters R0 and J* exhibits complex relations compared to the models that are proposed in the literature. The quadratic regression model proposed here has discriminated the most influencing parameters within the following approximated order, DLBL, HCI, Elderly, Tav, CO2, and WC as first order, interaction, and second order terms.ConclusionsThis modeling allowed the emergence of interaction terms that don't appear in similar studies; this led to emphasize more complex relationship between the infection spread and the socio-economic factors. Future works will focus on enriching the datasets and the optimization of the controlled parameters to short-term slowdown of similar pandemics.

Dataset Information

The short-term economic consequences of COVID-19: Exposure to disease, remote work and government response.

Publications

The short-term economic consequences of COVID-19: Exposure to disease, remote work and government response.

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